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Abstract

Semantic networks produced from human data have statistical properties that cannot be easily captured by spatial representations. We explore a probabilistic approach to semantic representation that explicitly models the probability with which words occurin diffrent contexts, and hence captures the probabilistic relationships between words. We show that this representation has statistical properties consistent with the large-scale structure of semantic networks constructed by humans, and trace the origins of these properties.

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Abstract

Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.

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Abstract

Natural Language is not only the most important means of communication between human beings, it is also used over historical periods for the preservation of cultural achievements and their transmission from one generation to the other. During the last few decades, the flod of digitalized information has been growing tremendously. This tendency will continue with the globalisation of information societies and with the growing importance of national and international computer networks. This is one reason why the theoretical understanding and the automated treatment of communication processes based on natural language have such a decisive social and economic impact. In this context, the semantic representation of knowledge originally formulated in natural language plays a central part, because it connects all components of natural language processing systems, be they the automatic understanding of natural language (analysis), the rational reasoning over knowledge bases, or the generation of natural language expressions from formal representations. This book presents a method for the semantic representation of natural language expressions (texts, sentences, phrases, etc.) which can be used as a universal knowledge representation paradigm in the human sciences, like linguistics, cognitive psychology, or philosophy of language, as well as in computational linguistics and in artificial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.